Data Mining
Learn Data Mining from basics in this free online training. This free Data Mining course is taught hands-on by experts. Learn about Data Description, Data Manipulation, Skewness & a lot more. Best for Beginners. Start now!
4.51
Intermediate
3.75 Hrs
39.6K+
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About this course
This Data Mining course will introduce you to prominent Data Mining concepts. The course begins by introducing you to data description concepts. You will understand the basics of data, data manipulation, and skewness using histograms in the first half of the course. You will then learn to visualize outliers using boxplots, correlation using scatter plots, and understand what machine learning is. You will also understand regression analysis, multiple linear regression, and logistic regression, with demonstrated examples in the latter part of this course. There is an assessment to evaluate your knowledge at the end of the course. Complete the course for free and avail your certificate. You can also study the attached materials for reference. After this free, self-paced, intermediate-level guide to Data Mining, you can enroll in the Data Science course and embark on your career with the professional Post Graduate certificate. Learn various concepts in depth with millions of aspirants across the globe!
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Course Outline
Data Description
You will learn mathematics concepts for data mining tasks such as statistics, its types, population, parameter, sample, mean, median, mode, normal distribution, interquartile range of IQR, and its upper and lower limits. This section comprehends a demonstration of the outlier concept at the end of the course for your better understanding.
Basic Data Understanding
You shall understand data and learn to infer insights from the datasets using the diabetes dataset in this section.
Data Manipulation
This section explains how to work with or manipulate the data with different methods in a given set to extract a particular range of values. You will also understand how a dataset not showing accurate data can be recognized and be replaced with the median since it does not get affected by outliers.
Skewness using Histogram/ Density Curves
You shall understand the outlier concept in-depth in this section. You will learn to detect and impute outliers and understand their working later in this section. You will also learn to infer/express data using the histogram.
Visualising outliers using boxplots
You will learn to express missing data and express data in box plots for simple representation and also understand outlier analysis concepts in this section.
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